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eirm: Explanatory Item Response Modeling for Dichotomous and Polytomous Items

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 22 Feb 2021Publisher:Zenodo
Authors: , Okan;

doi: 10.5281/zenodo.4556285 , 10.5281/zenodo.5153934 , 10.5281/zenodo.4556284

eirm: Explanatory Item Response Modeling for Dichotomous and Polytomous Items

Abstract

Analysis of dichotomous and polytomous response data using the explanatory item response modeling framework, as described in Stanke & Bulut (2019) <doi:10.21449/ijate.515085> and De Boeck & Wilson (2004) <doi:10.1007/978-1-4757-3990-9>. Generalized linear mixed modeling is used for estimating the effects of item-related and person-related variables on dichotomous and polytomous item responses.

Keywords

explanatory IRT, item response theory, eirm, glmm

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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